Positional Effects on Citation and Readership in arXiv

TL;DR

This study investigates how article position in arXiv announcements influences long-term citation impact and readership, revealing effects of intentional self-promotion and accidental visibility, with implications for impact metrics and recommender systems.

Contribution

The paper provides a detailed analysis of positional effects on citations and readership in arXiv, highlighting both self-promotion and visibility influences, and employs machine learning to link early readership to long-term impact.

Findings

Articles in top positions receive significantly more citations and downloads.

Self-promotion by authors contributes to higher impact for top-position articles.

Accidental early-position articles also benefit from increased visibility and impact.

Abstract

arXiv.org mediates contact with the literature for entire scholarly communities, both through provision of archival access and through daily email and web announcements of new materials, potentially many screenlengths long. We confirm and extend a surprising correlation between article position in these initial announcements, ordered by submission time, and later citation impact, due primarily to intentional "self-promotion" on the part of authors. A pure "visibility" effect was also present: the subset of articles accidentally in early positions fared measurably better in the long-term citation record than those lower down. Astrophysics articles announced in position 1, for example, overall received a median number of citations 83\% higher, while those there accidentally had a 44\% visibility boost. For two large subcommunities of theoretical high energy physics, hep-th and hep-ph articles announced in position 1 had median numbers of citations 50\% and 100\% larger than for positions 5--15, and the subsets there accidentally had visibility boosts of 38\% and 71\%. We also consider the positional effects on early readership. The median numbers of early full text downloads for astro-ph, hep-th, and hep-ph articles announced in position 1 were 82\%, 61\%, and 58\% higher than for lower positions, respectively, and those there accidentally had medians visibility-boosted by 53\%, 44\%, and 46\%. Finally, we correlate a variety of readership features with long-term citations, using machine learning methods, thereby extending previous results on the predictive power of early readership in a broader context. We conclude with some observations on impact metrics and dangers of recommender mechanisms.